In digital pathology, digital microscopic photographs are made of samples. The resulting digital images are then presented to a pathologist for evaluation. Digital pathology has several advantages compared to traditional pathology, in which samples are viewed directly through a microscope. First of all, digital pathology can increase the throughput. This becomes more and more relevant, since pathologists face increasing workloads. Secondly, digital pathology can provide means to improve the quality of the diagnosis. Studies indicate that the consistency of diagnoses by different pathologists can significantly improve if computer algorithms are used to assist the pathologist.
However, digital pathology may also have a few disadvantages. Currently, one of the disadvantages is that the focal plane is fixed. Digital scanners typically acquire only one image of the tissue. Adjustment of the focal plane is thereafter no longer possible. This can cause out-of-focus artifacts. However, it is also possible to acquire a plurality of different images, using different focal planes.
Besides this focusing problem, other artifacts, that commonly occur in pathology, also occur in digital pathologic images. Such artifacts may include:
Pressure effects.
Fixation artifacts (for example due to incorrect fixative used, contamination, or formation of acid formalin haematin pigment).
Inadequate dehydration (for example, water that remains trapped within the tissue).
Staining artifacts (for example due to an uncleaned water bath or uneven staining).
Objects that unintentionally appear in the image (for example, a microorganism or a hair).
Defocus artifacts.
Sensor noise.
A pathologist, by experience, is capable of recognizing such artifacts and ignoring them when interpreting an image. However, automatic image analysis programs may have difficulty processing an image containing one or more artifact(s).
Currently available digital analysis techniques perform normalization and nucleus detection for the complete tissue, or at least for a complete field of view.
US 2006/0014238 A1 discloses enhancing a digital image of a biological sample to which an immunohistochemical compound has been applied; removing predetermined types of unwanted cells in the enhanced digital image from consideration; identifying plural cells of interest in the enhanced digital image; identifying one or more areas of interest in the enhanced digital image; and removing cell artifacts from consideration in the one or more identified areas of interest, thereby automatically creating one or more enhanced areas of interest used for creating a medical diagnosis or prognosis.
M. Macenko, M. Niethammer, J. S. Marron, D. Borland, J. T. Woosley, X. Guan, C. Schmitt and N. E. Thomas, “A Method for Normalizing Histology Slides for Quantitative Analysis”, in IEEE Int. Symposium on Biomedical Imaging, 2009, hereinafter: Macenko et al., discloses two mechanisms for overcoming inconsistencies in the staining process, thereby bringing slides that were processed or stored under different conditions into a common, normalized space to enable improved quantitative analysis. The paper discloses an algorithm that automatically finds the correct stain vectors for the image and then performs the color deconvolution.
In other kinds of image processing, problems may also occur when processing an image that contains undesirable regions.